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Record W2579715061 · doi:10.1038/srep41027

In vivo detection of cucurbit[6]uril, a hyperpolarized xenon contrast agent for a xenon magnetic resonance imaging biosensor

2017· article· en· W2579715061 on OpenAlexafffund
Francis Hane, Tao Li, Peter S. Smylie, Raiili M. Pellizzari, Jennifer Anne Plata, Brenton DeBoef, Mitchell S. Albert

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAtomic and Subatomic Physics Research
Canadian institutionsNOSM UniversityLakehead UniversityThunder Bay Regional Research Institute
FundersLakehead UniversityUniversity of VictoriaBrightFocus Foundation
KeywordsXenonMagnetic resonance imagingIn vivoNuclear magnetic resonanceHyperpolarization (physics)ChemistryContrast (vision)MRI contrast agentSaturation (graph theory)Molecular imagingBiophysicsNuclear magnetic resonance spectroscopyMedicineRadiologyBiologyPhysicsOptics

Abstract

fetched live from OpenAlex

The Hyperpolarized gas Chemical Exchange Saturation Transfer (HyperCEST) Magnetic Resonance (MR) technique has the potential to increase the sensitivity of a hyperpolarized xenon-129 MRI contrast agent. Signal enhancement is accomplished by selectively depolarizing the xenon within a cage molecule which, upon exchange, reduces the signal in the dissolved phase pool. Herein we demonstrate the in vivo detection of the cucurbit[6]uril (CB6) contrast agent within the vasculature of a living rat. Our work may be used as a stepping stone towards using the HyperCEST technique as a molecular imaging modality.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.284
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations70
Published2017
Admission routes2
Has abstractyes

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